The increasing integration of renewable energy sources highlights the urgent need for grid flexibility,with buildings serving as key controllable loads.In this context,accurately quantifying building flexibility is es...The increasing integration of renewable energy sources highlights the urgent need for grid flexibility,with buildings serving as key controllable loads.In this context,accurately quantifying building flexibility is essential for enabling effective demand-side management and ensuring reliable grid operations.However,several challenges hinder this quantification.To address these issues,this study proposes a comprehensive flexibility quantification framework.First,a novel RC-Mapping model incorporating an Enumerate-Comparison Method is proposed.The RC-Mapping model can capture the thermal behavior of both the building and the air conditioning system,while the Enumerate-Comparison Method can initialize state parameters in the RC-Mapping model.Compared with the conventional approach,as validated by the experiment,the proposed method can substantially improve RMSE for indoor temperature prediction from 0.542℃to 0.266℃,and the MAPE for flexibility quantification from 27.58%to 10.98%.Second,the study introduces the power reduction-duration curve and temperature variation curves to characterize flexibility from both grid and building perspectives.Specifically,based on the analysis of the power reduction-duration curve,this study provides a systematic analysis of four sources of flexibility and their underlying mechanisms,including the thermal storage of the building,the thermal storage of the HVAC system,the increase of coefficient of performance(COP),and the reduction in cooling load.Finally,the study investigates the impact of uncertainties in COP and internal heat gains on flexibility quantification.According to the result,it is recommended to slightly underestimate the COP and overestimate the internal heat gain schedule to improve the accuracy of flexibility quantification.展开更多
基金the National Natural Science Foundation of China(No.52308104)the Shenzhen Science and Technology Program(Grant No.KCXST20221021111203007).
文摘The increasing integration of renewable energy sources highlights the urgent need for grid flexibility,with buildings serving as key controllable loads.In this context,accurately quantifying building flexibility is essential for enabling effective demand-side management and ensuring reliable grid operations.However,several challenges hinder this quantification.To address these issues,this study proposes a comprehensive flexibility quantification framework.First,a novel RC-Mapping model incorporating an Enumerate-Comparison Method is proposed.The RC-Mapping model can capture the thermal behavior of both the building and the air conditioning system,while the Enumerate-Comparison Method can initialize state parameters in the RC-Mapping model.Compared with the conventional approach,as validated by the experiment,the proposed method can substantially improve RMSE for indoor temperature prediction from 0.542℃to 0.266℃,and the MAPE for flexibility quantification from 27.58%to 10.98%.Second,the study introduces the power reduction-duration curve and temperature variation curves to characterize flexibility from both grid and building perspectives.Specifically,based on the analysis of the power reduction-duration curve,this study provides a systematic analysis of four sources of flexibility and their underlying mechanisms,including the thermal storage of the building,the thermal storage of the HVAC system,the increase of coefficient of performance(COP),and the reduction in cooling load.Finally,the study investigates the impact of uncertainties in COP and internal heat gains on flexibility quantification.According to the result,it is recommended to slightly underestimate the COP and overestimate the internal heat gain schedule to improve the accuracy of flexibility quantification.
